IS580 - Term Project
Ahmet Kuzubasli (1674431), Emre Doğan (2093656), Tayfun Eylen (1626183)
6/2/2018
Load Required Packages
First, we load all of required packages in this project at once. This allowed the project partners to load and work on the same versions of the packages.
1. Dataset and Introduction
We have choosen ECC dataset because it is about healthcare in what we are more professionally interested.
Objective: Early childhood caries (ECC) is a potentially severe disease affecting children all over the world [1]. The available findings are mostly based on a logistic regressionmodel, but data mining could be used to extract moreinformation from the same data set. In the paper, authors implement association rule mining for interpretability. While interpretability of the model is important, we seek other methods for classification and clustering with better performance.
Secondly, we import the training, test and validation splits of ECC datasets.
2. Descriptrive Statistics
#READ DATA
TRAIN = read.csv("./ECC_train.csv")
VALIDATION = read.csv("./ECC_validation.csv")
TEST = read.csv("./ECC_test.csv")## 3. Classification Methods
options(knitr.kable.NA = '')
#summary of the dataset gives us the brief information.
kable(summary(TRAIN)) | CITY | CHILD_ETHNICITY | CHILD_AGE | CHILD_GENDER | CHILD_SERBIAN_LANGUAGE | MOTHER_AGE | MARITAL_STATUS | MOTHER_ETHNICITY | MOTHER_SERBIAN_LANGUAGE | NUMBER_OF_CHILDREN | BIRTH_ORDER | MOTHER_EDUCATION_LEVEL | MOTHER_EMPLOYMENT_STATUS | QUALITY_OF_HOUSING | HOUSING_CONDITIONS | HOUSEHOLD_MONTHLY_INCOME | BIRTH_WEIGHT | BREASTFEEDING | BREASTFEEDING_FREQUENCY | BREASTFEEDING_DURING_NIGHT | BOTTLE_FEEDING | INFANT_FORMULAS | ADDITIONAL_FOOD_SWEETENING | CHILD_FLUORIDE_SUPPLEMENTS | CHILD_FLUORIDE_TOOTHPASTE | CHILD_ORAL_HYGIENE | CHILD_TOOTH_BRUSHING | DIARRHEA_DURING_INFANCY | MEDICAL_SYRUPS | CHILD_FIRST_DENTIST_VISIT | SWEETS_DURING_PREGNANCY | FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | ORAL_HEALTH_DURING_PREGNANCY | MOTHER_HEALTH_AWARENESS | FATHER_HEALTH_AWARENESS | ECC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NOVI_SAD :79 | Min. :1.000 | Min. :1.00 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. : 1.00 | Min. :1.000 | Min. :1.0 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.0 | Min. :1.000 | Min. :1.000 | Min. :1 | Min. : 1 | Min. : 1.00 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.0 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | Min. :1.000 | |
| BACKA_PALANKA:42 | 1st Qu.:1.000 | 1st Qu.:3.00 | 1st Qu.:1.000 | 1st Qu.:1.000 | 1st Qu.:2.000 | 1st Qu.:1.000 | 1st Qu.: 1.00 | 1st Qu.:1.000 | 1st Qu.:1.0 | 1st Qu.:1.000 | 1st Qu.:3.000 | 1st Qu.:2.000 | 1st Qu.:1.000 | 1st Qu.:1.0 | 1st Qu.:3.000 | 1st Qu.:2.000 | 1st Qu.:1 | 1st Qu.: 2 | 1st Qu.: 1.00 | 1st Qu.:2.000 | 1st Qu.:1.000 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:1.000 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:2.0 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:1.000 | 1st Qu.:2.000 | 1st Qu.:2.000 | 1st Qu.:1.000 | |
| KISAC :29 | Median :1.000 | Median :3.00 | Median :1.000 | Median :1.000 | Median :3.000 | Median :1.000 | Median : 1.00 | Median :2.000 | Median :2.0 | Median :1.000 | Median :3.000 | Median :3.000 | Median :2.000 | Median :1.0 | Median :4.000 | Median :2.000 | Median :2 | Median : 2 | Median : 1.00 | Median :2.000 | Median :2.000 | Median :2.000 | Median :3.000 | Median :1.000 | Median :2.000 | Median :2.000 | Median :2.0 | Median :2.000 | Median :3.000 | Median :2.000 | Median :3.000 | Median :2.000 | Median :2.000 | Median :2.000 | Median :2.000 | |
| RUSKI_KRSTUR :23 | Mean :2.167 | Mean :3.13 | Mean :1.473 | Mean :1.134 | Mean :2.427 | Mean :1.238 | Mean : 22.91 | Mean :1.732 | Mean :1.9 | Mean :1.678 | Mean :3.008 | Mean :2.427 | Mean :1.849 | Mean :1.1 | Mean :3.347 | Mean :1.908 | Mean :2 | Mean :119 | Mean : 93.01 | Mean :2.427 | Mean :1.565 | Mean :2.297 | Mean :2.707 | Mean :1.397 | Mean :1.879 | Mean :2.146 | Mean :1.9 | Mean :2.423 | Mean :3.159 | Mean :1.854 | Mean :2.431 | Mean :1.799 | Mean :2.059 | Mean :1.874 | Mean :1.703 | |
| TITEL :22 | 3rd Qu.:3.000 | 3rd Qu.:4.00 | 3rd Qu.:2.000 | 3rd Qu.:1.000 | 3rd Qu.:3.000 | 3rd Qu.:1.000 | 3rd Qu.: 3.00 | 3rd Qu.:2.000 | 3rd Qu.:2.0 | 3rd Qu.:2.000 | 3rd Qu.:3.000 | 3rd Qu.:3.000 | 3rd Qu.:3.000 | 3rd Qu.:1.0 | 3rd Qu.:4.000 | 3rd Qu.:2.000 | 3rd Qu.:3 | 3rd Qu.: 3 | 3rd Qu.: 1.00 | 3rd Qu.:3.000 | 3rd Qu.:2.000 | 3rd Qu.:3.000 | 3rd Qu.:3.000 | 3rd Qu.:2.000 | 3rd Qu.:2.000 | 3rd Qu.:3.000 | 3rd Qu.:2.0 | 3rd Qu.:3.000 | 3rd Qu.:4.000 | 3rd Qu.:2.000 | 3rd Qu.:3.000 | 3rd Qu.:2.000 | 3rd Qu.:2.000 | 3rd Qu.:2.000 | 3rd Qu.:2.000 | |
| TEMERIN :17 | Max. :7.000 | Max. :5.00 | Max. :2.000 | Max. :2.000 | Max. :3.000 | Max. :3.000 | Max. :999.00 | Max. :2.000 | Max. :3.0 | Max. :3.000 | Max. :4.000 | Max. :4.000 | Max. :3.000 | Max. :2.0 | Max. :5.000 | Max. :2.000 | Max. :4 | Max. :999 | Max. :999.00 | Max. :4.000 | Max. :2.000 | Max. :3.000 | Max. :3.000 | Max. :3.000 | Max. :3.000 | Max. :4.000 | Max. :2.0 | Max. :3.000 | Max. :4.000 | Max. :3.000 | Max. :3.000 | Max. :3.000 | Max. :3.000 | Max. :3.000 | Max. :2.000 | |
| (Other) :27 |
It can be seen that all the attributes except the “CITY” have numerical distribution.
Three attributes have thair maximum values as ‘999’. This value is meaningless and gives the ‘NA’ attribute. These ‘999’ values may correspond to problems and should be considered as missing data and be replaced.
Most of the data corresponds to ordinal data and should be considered as so. They will be converted with ‘ordered()’ function.
for (col in 2:ncol(TRAIN)) {
hist(TRAIN[,col], main = paste("Histogram of", colnames(TRAIN)[col]))
}
All the distributions of attributes are observed and the problems with the attributes having ‘999’ values are observed.
Most of the attributes have ordinal characteristics and very few classes. This situation can be observed from the histograms.
for (col in 2:ncol(TRAIN)) {
qqnorm(TRAIN[,col], main = paste("Normal QQ Plot of ",colnames(TRAIN)[col])); qqline(TRAIN[,col])
}
The Q-Q plots give a strong idea about the closeness of an attribute to the normal one. If the data is normally distributed, the points in the QQ-normal plot lie on a straight diagonal line.
As most of our data is type of nominal, it is not expected to have a normal data distribution in attributes. But still, It is possible to observe the dsitribution of nominal data labels in these Q-Q plots.
A-3. Descriptive Location Measures for Each of the Numerical Attributes
Control Tendency
- We already achieved mean and median values of each attributes with summary() command.
- Besides that, geometric mean is an important measure of the central tendency.
geomean = matrix(0,36,1)
for (col in 2:ncol(TRAIN)) {
geomean[col] = exp(mean(log(TRAIN[,col])))
}
#geomean
geomean_vector <- data.frame(geomean)
row.names(geomean_vector) <- colnames(TRAIN)
kable(geomean_vector,row.names = TRUE)| geomean | |
|---|---|
| CITY | 0.000000 |
| CHILD_ETHNICITY | 1.758148 |
| CHILD_AGE | 3.018245 |
| CHILD_GENDER | 1.387803 |
| CHILD_SERBIAN_LANGUAGE | 1.097249 |
| MOTHER_AGE | 2.285236 |
| MARITAL_STATUS | 1.158650 |
| MOTHER_ETHNICITY | 1.887356 |
| MOTHER_SERBIAN_LANGUAGE | 1.661191 |
| NUMBER_OF_CHILDREN | 1.741823 |
| BIRTH_ORDER | 1.513556 |
| MOTHER_EDUCATION_LEVEL | 2.908949 |
| MOTHER_EMPLOYMENT_STATUS | 2.217976 |
| QUALITY_OF_HOUSING | 1.632377 |
| HOUSING_CONDITIONS | 1.072084 |
| HOUSEHOLD_MONTHLY_INCOME | 3.108337 |
| BIRTH_WEIGHT | 1.876377 |
| BREASTFEEDING | 1.754348 |
| BREASTFEEDING_FREQUENCY | 4.413374 |
| BREASTFEEDING_DURING_NIGHT | 2.084179 |
| BOTTLE_FEEDING | 2.240267 |
| INFANT_FORMULAS | 1.479237 |
| ADDITIONAL_FOOD_SWEETENING | 2.165548 |
| CHILD_FLUORIDE_SUPPLEMENTS | 2.638544 |
| CHILD_FLUORIDE_TOOTHPASTE | 1.272027 |
| CHILD_ORAL_HYGIENE | 1.799259 |
| CHILD_TOOTH_BRUSHING | 1.968312 |
| DIARRHEA_DURING_INFANCY | 1.865525 |
| MEDICAL_SYRUPS | 2.368098 |
| CHILD_FIRST_DENTIST_VISIT | 2.985174 |
| SWEETS_DURING_PREGNANCY | 1.783182 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 2.259006 |
| ORAL_HEALTH_DURING_PREGNANCY | 1.650330 |
| MOTHER_HEALTH_AWARENESS | 1.992148 |
| FATHER_HEALTH_AWARENESS | 1.783284 |
| ECC | 1.627806 |
Dispersion
Besides the central tendency, the fact that how closely the data fall about the center is another issue. We need to figure out the spread pattern around the center.
- Range:
rangeVector = matrix(0,36,1)
for (col in 2:ncol(TRAIN)) {
rangeVector[col] = max(TRAIN[,col], na.rm = TRUE)-min(TRAIN[,col], na.rm = TRUE)
}
range_Vector <- data.frame(rangeVector)
row.names(range_Vector) <- colnames(TRAIN)
kable(range_Vector,row.names = TRUE)| rangeVector | |
|---|---|
| CITY | 0 |
| CHILD_ETHNICITY | 6 |
| CHILD_AGE | 4 |
| CHILD_GENDER | 1 |
| CHILD_SERBIAN_LANGUAGE | 1 |
| MOTHER_AGE | 2 |
| MARITAL_STATUS | 2 |
| MOTHER_ETHNICITY | 998 |
| MOTHER_SERBIAN_LANGUAGE | 1 |
| NUMBER_OF_CHILDREN | 2 |
| BIRTH_ORDER | 2 |
| MOTHER_EDUCATION_LEVEL | 3 |
| MOTHER_EMPLOYMENT_STATUS | 3 |
| QUALITY_OF_HOUSING | 2 |
| HOUSING_CONDITIONS | 1 |
| HOUSEHOLD_MONTHLY_INCOME | 4 |
| BIRTH_WEIGHT | 1 |
| BREASTFEEDING | 3 |
| BREASTFEEDING_FREQUENCY | 998 |
| BREASTFEEDING_DURING_NIGHT | 998 |
| BOTTLE_FEEDING | 3 |
| INFANT_FORMULAS | 1 |
| ADDITIONAL_FOOD_SWEETENING | 2 |
| CHILD_FLUORIDE_SUPPLEMENTS | 2 |
| CHILD_FLUORIDE_TOOTHPASTE | 2 |
| CHILD_ORAL_HYGIENE | 2 |
| CHILD_TOOTH_BRUSHING | 3 |
| DIARRHEA_DURING_INFANCY | 1 |
| MEDICAL_SYRUPS | 2 |
| CHILD_FIRST_DENTIST_VISIT | 3 |
| SWEETS_DURING_PREGNANCY | 2 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 2 |
| ORAL_HEALTH_DURING_PREGNANCY | 2 |
| MOTHER_HEALTH_AWARENESS | 2 |
| FATHER_HEALTH_AWARENESS | 2 |
| ECC | 1 |
- Interquantile Range
iqc = matrix(0,36,1)
for (col in 2:ncol(TRAIN)) {
iqc[col] = IQR(TRAIN[,col])
}
iqr_vector <- data.frame(iqc)
row.names(iqr_vector) <- colnames(TRAIN)
kable(iqr_vector, row.names = TRUE)| iqc | |
|---|---|
| CITY | 0 |
| CHILD_ETHNICITY | 2 |
| CHILD_AGE | 1 |
| CHILD_GENDER | 1 |
| CHILD_SERBIAN_LANGUAGE | 0 |
| MOTHER_AGE | 1 |
| MARITAL_STATUS | 0 |
| MOTHER_ETHNICITY | 2 |
| MOTHER_SERBIAN_LANGUAGE | 1 |
| NUMBER_OF_CHILDREN | 1 |
| BIRTH_ORDER | 1 |
| MOTHER_EDUCATION_LEVEL | 0 |
| MOTHER_EMPLOYMENT_STATUS | 1 |
| QUALITY_OF_HOUSING | 2 |
| HOUSING_CONDITIONS | 0 |
| HOUSEHOLD_MONTHLY_INCOME | 1 |
| BIRTH_WEIGHT | 0 |
| BREASTFEEDING | 2 |
| BREASTFEEDING_FREQUENCY | 1 |
| BREASTFEEDING_DURING_NIGHT | 0 |
| BOTTLE_FEEDING | 1 |
| INFANT_FORMULAS | 1 |
| ADDITIONAL_FOOD_SWEETENING | 1 |
| CHILD_FLUORIDE_SUPPLEMENTS | 1 |
| CHILD_FLUORIDE_TOOTHPASTE | 1 |
| CHILD_ORAL_HYGIENE | 0 |
| CHILD_TOOTH_BRUSHING | 1 |
| DIARRHEA_DURING_INFANCY | 0 |
| MEDICAL_SYRUPS | 1 |
| CHILD_FIRST_DENTIST_VISIT | 2 |
| SWEETS_DURING_PREGNANCY | 0 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 1 |
| ORAL_HEALTH_DURING_PREGNANCY | 1 |
| MOTHER_HEALTH_AWARENESS | 0 |
| FATHER_HEALTH_AWARENESS | 0 |
| ECC | 1 |
- Variance
variance = matrix(0,36,1)
for (col in 2:ncol(TRAIN)) {
variance[col] = var(TRAIN[,col])
}
var_vector <- data.frame(variance)
row.names(var_vector) <- colnames(TRAIN)
kable(var_vector, row.names = TRUE)| variance | |
|---|---|
| CITY | 0.000000e+00 |
| CHILD_ETHNICITY | 2.198762e+00 |
| CHILD_AGE | 6.091558e-01 |
| CHILD_GENDER | 2.503077e-01 |
| CHILD_SERBIAN_LANGUAGE | 1.164516e-01 |
| MOTHER_AGE | 5.229774e-01 |
| MARITAL_STATUS | 3.084280e-01 |
| MOTHER_ETHNICITY | 2.044553e+04 |
| MOTHER_SERBIAN_LANGUAGE | 1.968988e-01 |
| NUMBER_OF_CHILDREN | 5.697057e-01 |
| BIRTH_ORDER | 6.058507e-01 |
| MOTHER_EDUCATION_LEVEL | 4.537112e-01 |
| MOTHER_EMPLOYMENT_STATUS | 7.414648e-01 |
| QUALITY_OF_HOUSING | 8.175521e-01 |
| HOUSING_CONDITIONS | 9.071410e-02 |
| HOUSEHOLD_MONTHLY_INCOME | 1.194016e+00 |
| BIRTH_WEIGHT | 8.392810e-02 |
| BREASTFEEDING | 1.058823e+00 |
| BREASTFEEDING_FREQUENCY | 1.032018e+05 |
| BREASTFEEDING_DURING_NIGHT | 8.356663e+04 |
| BOTTLE_FEEDING | 8.254984e-01 |
| INFANT_FORMULAS | 2.468268e-01 |
| ADDITIONAL_FOOD_SWEETENING | 4.954116e-01 |
| CHILD_FLUORIDE_SUPPLEMENTS | 2.752013e-01 |
| CHILD_FLUORIDE_TOOTHPASTE | 4.841954e-01 |
| CHILD_ORAL_HYGIENE | 2.583243e-01 |
| CHILD_TOOTH_BRUSHING | 7.557751e-01 |
| DIARRHEA_DURING_INFANCY | 9.071410e-02 |
| MEDICAL_SYRUPS | 2.618403e-01 |
| CHILD_FIRST_DENTIST_VISIT | 8.653704e-01 |
| SWEETS_DURING_PREGNANCY | 2.179600e-01 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 6.160121e-01 |
| ORAL_HEALTH_DURING_PREGNANCY | 5.309237e-01 |
| MOTHER_HEALTH_AWARENESS | 2.486551e-01 |
| FATHER_HEALTH_AWARENESS | 3.035055e-01 |
| ECC | 2.096973e-01 |
- Coefficient of Variance
CV = matrix(0,36,1)
for (col in 2:ncol(TRAIN)) {
CV[col] = sd(TRAIN[,col], na.rm=TRUE)/mean(TRAIN[,col], na.rm=TRUE)*100
}
CV_vector <- data.frame(CV)
row.names(CV_vector) <- colnames(TRAIN)
kable(CV_vector, row.names = TRUE)| CV | |
|---|---|
| CITY | 0.00000 |
| CHILD_ETHNICITY | 68.41594 |
| CHILD_AGE | 24.93794 |
| CHILD_GENDER | 33.96975 |
| CHILD_SERBIAN_LANGUAGE | 30.09548 |
| MOTHER_AGE | 29.79966 |
| MARITAL_STATUS | 44.84180 |
| MOTHER_ETHNICITY | 624.07043 |
| MOTHER_SERBIAN_LANGUAGE | 25.61646 |
| NUMBER_OF_CHILDREN | 39.73446 |
| BIRTH_ORDER | 46.39128 |
| MOTHER_EDUCATION_LEVEL | 22.39024 |
| MOTHER_EMPLOYMENT_STATUS | 35.48258 |
| QUALITY_OF_HOUSING | 48.89150 |
| HOUSING_CONDITIONS | 27.37030 |
| HOUSEHOLD_MONTHLY_INCOME | 32.64472 |
| BIRTH_WEIGHT | 15.18402 |
| BREASTFEEDING | 51.44958 |
| BREASTFEEDING_FREQUENCY | 270.01530 |
| BREASTFEEDING_DURING_NIGHT | 310.80960 |
| BOTTLE_FEEDING | 37.43933 |
| INFANT_FORMULAS | 31.74844 |
| ADDITIONAL_FOOD_SWEETENING | 30.64140 |
| CHILD_FLUORIDE_SUPPLEMENTS | 19.37844 |
| CHILD_FLUORIDE_TOOTHPASTE | 49.79225 |
| CHILD_ORAL_HYGIENE | 27.05417 |
| CHILD_TOOTH_BRUSHING | 40.50203 |
| DIARRHEA_DURING_INFANCY | 15.85548 |
| MEDICAL_SYRUPS | 21.12212 |
| CHILD_FIRST_DENTIST_VISIT | 29.44774 |
| SWEETS_DURING_PREGNANCY | 25.18735 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 32.28616 |
| ORAL_HEALTH_DURING_PREGNANCY | 40.49911 |
| MOTHER_HEALTH_AWARENESS | 24.22320 |
| FATHER_HEALTH_AWARENESS | 29.39024 |
| ECC | 26.89056 |
Coefficient of variance is a better parameter to see the behaviour of the data. Because it gives more logical results in the attributes with different scales.
Correlation & Covariance
Correlation and Covariance matrixes will be very helpful in our Feature Selection process. It is not wise to use two highly correlated attributes in the same model. Because, this situation would result with overfitting problems.
options(knitr.kable.NA = '')
NUM=data.frame(TRAIN[2:36])
# correlations/covariance
kable(cov(NUM))| CHILD_ETHNICITY | CHILD_AGE | CHILD_GENDER | CHILD_SERBIAN_LANGUAGE | MOTHER_AGE | MARITAL_STATUS | MOTHER_ETHNICITY | MOTHER_SERBIAN_LANGUAGE | NUMBER_OF_CHILDREN | BIRTH_ORDER | MOTHER_EDUCATION_LEVEL | MOTHER_EMPLOYMENT_STATUS | QUALITY_OF_HOUSING | HOUSING_CONDITIONS | HOUSEHOLD_MONTHLY_INCOME | BIRTH_WEIGHT | BREASTFEEDING | BREASTFEEDING_FREQUENCY | BREASTFEEDING_DURING_NIGHT | BOTTLE_FEEDING | INFANT_FORMULAS | ADDITIONAL_FOOD_SWEETENING | CHILD_FLUORIDE_SUPPLEMENTS | CHILD_FLUORIDE_TOOTHPASTE | CHILD_ORAL_HYGIENE | CHILD_TOOTH_BRUSHING | DIARRHEA_DURING_INFANCY | MEDICAL_SYRUPS | CHILD_FIRST_DENTIST_VISIT | SWEETS_DURING_PREGNANCY | FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | ORAL_HEALTH_DURING_PREGNANCY | MOTHER_HEALTH_AWARENESS | FATHER_HEALTH_AWARENESS | ECC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CHILD_ETHNICITY | 2.1987624 | -0.2024718 | 0.0339826 | 0.1581695 | -0.2397947 | 0.0481523 | 52.6828346 | -0.1776836 | 0.2899863 | 0.3062480 | -0.5056081 | -0.4834921 | 0.1135509 | 0.1806019 | -0.7306354 | -0.0727647 | 0.2857143 | 4.749161e+01 | 1.3431314 | 0.0333146 | -0.0403115 | 0.0467107 | 0.0702331 | 0.2147076 | 0.1758553 | 0.2232868 | -0.1511902 | 0.0760346 | 0.2295805 | -0.0888330 | 0.0830315 | 0.3362751 | -0.2241307 | -0.3318449 | -0.1265427 |
| CHILD_AGE | -0.2024718 | 0.6091558 | 0.0266517 | -0.0300447 | 0.1040751 | 0.0109525 | 9.6080834 | 0.0138708 | -0.0079287 | -0.0336662 | 0.0703386 | 0.0788650 | -0.0434056 | -0.0256848 | 0.1480433 | 0.0119897 | -0.0672269 | -1.105976e+01 | -7.7784009 | -0.0303787 | 0.0440737 | -0.0134841 | -0.0416828 | -0.0391688 | -0.0178088 | -0.1661334 | 0.0130797 | -0.0340354 | -0.1005415 | 0.0190746 | -0.0309237 | -0.0452692 | 0.0427903 | 0.0415597 | 0.0344925 |
| CHILD_GENDER | 0.0339826 | 0.0266517 | 0.2503077 | -0.0131500 | -0.0639745 | 0.0086143 | -5.6851728 | -0.0325235 | 0.0056608 | -0.0151014 | -0.0207799 | -0.0051510 | 0.0421047 | 0.0111459 | 0.0073837 | -0.0025140 | -0.0336134 | -1.356791e+01 | -14.2560740 | 0.0368658 | 0.0007208 | 0.0060124 | -0.0164024 | 0.0087550 | -0.0012130 | -0.0485215 | 0.0056608 | -0.0199712 | -0.0208678 | 0.0233114 | 0.0558876 | -0.0096867 | -0.0110052 | 0.0091769 | 0.0444077 |
| CHILD_SERBIAN_LANGUAGE | 0.1581695 | -0.0300447 | -0.0131500 | 0.1164516 | -0.0447769 | 0.0057487 | -2.6184382 | -0.0228192 | 0.0555184 | 0.0559228 | -0.0725537 | -0.0615836 | 0.0160508 | 0.0159101 | -0.0887100 | -0.0128336 | 0.0000000 | -3.126877e+00 | -3.9717134 | 0.0014416 | -0.0087198 | 0.0188812 | 0.0099680 | 0.0137829 | 0.0247178 | 0.0223269 | -0.0243135 | -0.0148026 | 0.0626560 | 0.0154882 | 0.0008790 | 0.0354066 | -0.0246827 | -0.0167364 | -0.0272846 |
| MOTHER_AGE | -0.2397947 | 0.1040751 | -0.0639745 | -0.0447769 | 0.5229774 | -0.0517914 | 3.3317746 | 0.0601420 | 0.0304314 | 0.0708484 | 0.1728842 | 0.1742379 | -0.1245209 | -0.0724482 | 0.2713336 | 0.0562568 | -0.0378151 | 1.662213e-01 | 6.7569178 | -0.0064344 | -0.0067860 | 0.0533561 | -0.0467459 | -0.0905207 | -0.0782497 | -0.0585598 | 0.0514398 | 0.0121655 | -0.0723427 | 0.0333497 | -0.0880595 | -0.1114061 | 0.0967441 | 0.0790057 | 0.0096691 |
| MARITAL_STATUS | 0.0481523 | 0.0109525 | 0.0086143 | 0.0057487 | -0.0517914 | 0.3084280 | 3.4160015 | -0.0535143 | -0.0305721 | -0.0194789 | -0.0482226 | -0.0349847 | 0.0276713 | 0.0347737 | -0.0789705 | 0.0052389 | 0.0294118 | 1.392198e+01 | 7.3551387 | -0.0223797 | -0.0050280 | -0.0081221 | 0.0113217 | 0.0434584 | 0.0710770 | 0.0951795 | -0.0221687 | -0.0675961 | 0.0291481 | -0.0111459 | 0.0564502 | 0.0018811 | -0.0644492 | -0.0329630 | -0.0212897 |
| MOTHER_ETHNICITY | 52.6828346 | 9.6080834 | -5.6851728 | -2.6184382 | 3.3317746 | 3.4160015 | 20445.5258606 | 1.2410780 | 2.3230723 | 2.7782954 | -0.6589255 | -1.0085616 | -0.9502655 | -1.9113076 | 4.5684575 | 1.8406174 | 4.4117647 | 1.753670e+03 | -1927.9782532 | -0.5337717 | -3.4669667 | 2.2320945 | 1.9909637 | 0.2367533 | -1.4686896 | -2.8484231 | -2.2147428 | -0.3912837 | 1.0518442 | -5.4078795 | -4.8317218 | 0.2974052 | 2.7656728 | -10.2085546 | -6.4421785 |
| MOTHER_SERBIAN_LANGUAGE | -0.1776836 | 0.0138708 | -0.0325235 | -0.0228192 | 0.0601420 | -0.0535143 | 1.2410780 | 0.1968988 | -0.0732218 | -0.0446187 | 0.0694772 | 0.0937555 | -0.0363032 | -0.0402236 | 0.1101930 | 0.0172638 | -0.0084034 | 6.266358e+00 | 3.7417461 | -0.0196899 | 0.0216413 | -0.0251573 | -0.0241377 | -0.0611793 | -0.0536374 | -0.0866707 | 0.0234169 | -0.0040083 | -0.0412784 | 0.0068387 | -0.0311698 | -0.0414015 | 0.0409620 | 0.0166661 | -0.0000527 |
| NUMBER_OF_CHILDREN | 0.2899863 | -0.0079287 | 0.0056608 | 0.0555184 | 0.0304314 | -0.0305721 | 2.3230723 | -0.0732218 | 0.5697057 | 0.4759151 | -0.1504167 | -0.2174677 | 0.0604409 | 0.0689498 | -0.2885095 | -0.0260891 | -0.0420168 | -9.124380e+00 | -11.7260469 | 0.0304314 | 0.0065399 | -0.0036567 | 0.0166837 | 0.0064695 | 0.0591927 | 0.0483809 | -0.0521430 | 0.0005977 | 0.1714954 | -0.0525825 | -0.0069618 | 0.0973946 | -0.1159418 | -0.0798847 | -0.0383601 |
| BIRTH_ORDER | 0.3062480 | -0.0336662 | -0.0151014 | 0.0559228 | 0.0708484 | -0.0194789 | 2.7782954 | -0.0446187 | 0.4759151 | 0.6058507 | -0.1527548 | -0.2064625 | 0.0437045 | 0.0661018 | -0.2657959 | 0.0038325 | -0.0042017 | -4.066946e+00 | -8.0309061 | 0.0162266 | 0.0020745 | 0.0204810 | 0.0102844 | 0.0025491 | 0.0783904 | 0.0893956 | -0.0408917 | -0.0313456 | 0.1186667 | -0.0347737 | -0.0244366 | 0.1156957 | -0.1070989 | -0.0826272 | -0.0666995 |
| MOTHER_EDUCATION_LEVEL | -0.5056081 | 0.0703386 | -0.0207799 | -0.0725537 | 0.1728842 | -0.0482226 | -0.6589255 | 0.0694772 | -0.1504167 | -0.1527548 | 0.4537112 | 0.2989346 | -0.1247846 | -0.1016842 | 0.4634682 | 0.0427903 | -0.0546218 | -9.432562e+00 | -0.7563728 | 0.0342288 | 0.0120601 | 0.0563271 | -0.0521606 | -0.1083823 | -0.0998207 | -0.1188777 | 0.0890791 | -0.0287613 | -0.1399916 | 0.0642558 | -0.0750501 | -0.1705812 | 0.1759783 | 0.1607187 | 0.0823283 |
| MOTHER_EMPLOYMENT_STATUS | -0.4834921 | 0.0788650 | -0.0051510 | -0.0615836 | 0.1742379 | -0.0349847 | -1.0085616 | 0.0937555 | -0.2174677 | -0.2064625 | 0.2989346 | 0.7414648 | -0.1287226 | -0.1102634 | 0.5192328 | 0.0646602 | 0.0126050 | 8.527566e+00 | 15.1098590 | 0.0439858 | 0.0268275 | 0.0953729 | -0.0593509 | -0.1031258 | -0.0740480 | -0.0837699 | 0.0766499 | 0.0037622 | -0.1521747 | 0.0375514 | -0.0418410 | -0.1366162 | 0.1765761 | 0.1630393 | 0.0852994 |
| QUALITY_OF_HOUSING | 0.1135509 | -0.0434056 | 0.0421047 | 0.0160508 | -0.1245209 | 0.0276713 | -0.9502655 | -0.0363032 | 0.0604409 | 0.0437045 | -0.1247846 | -0.1287226 | 0.8175521 | 0.0488028 | -0.2037727 | -0.0265286 | 0.0546218 | -7.444974e+00 | -11.2382300 | -0.0488907 | -0.0111986 | 0.0239267 | 0.0691431 | 0.0349144 | 0.0110580 | 0.0431595 | -0.0277944 | 0.0303084 | 0.0198481 | 0.0324707 | 0.1282128 | 0.0914701 | -0.0793748 | -0.0778102 | -0.0365318 |
| HOUSING_CONDITIONS | 0.1806019 | -0.0256848 | 0.0111459 | 0.0159101 | -0.0724482 | 0.0347737 | -1.9113076 | -0.0402236 | 0.0689498 | 0.0661018 | -0.1016842 | -0.1102634 | 0.0488028 | 0.0907141 | -0.1358602 | -0.0159277 | 0.0420168 | 8.008509e-01 | -5.0638691 | 0.0073837 | -0.0065399 | 0.0120601 | 0.0253331 | 0.0649590 | 0.0626560 | 0.0692662 | -0.0444956 | 0.0036040 | 0.0427903 | -0.0272494 | 0.0531803 | 0.0916810 | -0.0773355 | -0.0671741 | -0.0120601 |
| HOUSEHOLD_MONTHLY_INCOME | -0.7306354 | 0.1480433 | 0.0073837 | -0.0887100 | 0.2713336 | -0.0789705 | 4.5684575 | 0.1101930 | -0.2885095 | -0.2657959 | 0.4634682 | 0.5192328 | -0.2037727 | -0.1358602 | 1.1940157 | 0.0993284 | -0.0588235 | -2.405427e+01 | -6.8810696 | 0.0738546 | 0.0509124 | 0.1400970 | -0.0323125 | -0.1596287 | -0.0879364 | -0.1309026 | 0.1442636 | -0.0381316 | -0.2445238 | 0.0846841 | -0.0830667 | -0.2072712 | 0.2484793 | 0.2118421 | 0.0951971 |
| BIRTH_WEIGHT | -0.0727647 | 0.0119897 | -0.0025140 | -0.0128336 | 0.0562568 | 0.0052389 | 1.8406174 | 0.0172638 | -0.0260891 | 0.0038325 | 0.0427903 | 0.0646602 | -0.0265286 | -0.0159277 | 0.0993284 | 0.0839281 | 0.0000000 | -1.783833e+00 | 0.1058155 | 0.0058366 | 0.0059949 | 0.0022503 | 0.0065399 | -0.0094758 | -0.0112162 | 0.0177385 | 0.0201294 | -0.0113568 | 0.0020921 | 0.0158750 | -0.0063816 | -0.0311698 | 0.0180198 | 0.0178088 | 0.0271615 |
| BREASTFEEDING | 0.2857143 | -0.0672269 | -0.0336134 | 0.0000000 | -0.0378151 | 0.0294118 | 4.4117647 | -0.0084034 | -0.0420168 | -0.0042017 | -0.0546218 | 0.0126050 | 0.0546218 | 0.0420168 | -0.0588235 | 0.0000000 | 1.0588235 | 2.220378e+02 | 184.4873950 | -0.1722689 | 0.0210084 | 0.0672269 | 0.0294118 | 0.0714286 | 0.0000000 | 0.1470588 | -0.0420168 | -0.0042017 | -0.0126050 | -0.0126050 | 0.0714286 | 0.1176471 | -0.0588235 | -0.0630252 | -0.0252101 |
| BREASTFEEDING_FREQUENCY | 47.4916142 | -11.0597553 | -13.5679125 | -3.1268767 | 0.1662213 | 13.9219788 | 1753.6700538 | 6.2663584 | -9.1243803 | -4.0669456 | -9.4325621 | 8.5275658 | -7.4449738 | 0.8008509 | -24.0542702 | -1.7838332 | 222.0378151 | 1.032018e+05 | 81188.4077740 | -158.8968039 | -41.0781970 | 2.8436236 | -7.5073837 | 20.4049787 | -15.0492775 | 24.7810028 | -0.8092542 | 0.7039309 | 27.4577898 | -12.1717591 | -4.3966984 | 11.0369537 | -15.2968426 | -2.0703913 | -2.9024472 |
| BREASTFEEDING_DURING_NIGHT | 1.3431314 | -7.7784009 | -14.2560740 | -3.9717134 | 6.7569178 | 7.3551387 | -1927.9782532 | 3.7417461 | -11.7260469 | -8.0309061 | -0.7563728 | 15.1098590 | -11.2382300 | -5.0638691 | -6.8810696 | 0.1058155 | 184.4873950 | 8.118841e+04 | 83566.6301818 | -127.4657712 | -31.1560072 | 1.9344784 | -2.3336732 | 9.5008614 | -18.1670476 | 20.0365845 | 0.8831968 | 7.1224992 | 10.4734538 | 0.9171970 | -6.2053022 | -2.4352871 | -9.5845259 | 3.2027355 | -1.9302767 |
| BOTTLE_FEEDING | 0.0333146 | -0.0303787 | 0.0368658 | 0.0014416 | -0.0064344 | -0.0223797 | -0.5337717 | -0.0196899 | 0.0304314 | 0.0162266 | 0.0342288 | 0.0439858 | -0.0488907 | 0.0073837 | 0.0738546 | 0.0058366 | -0.1722689 | -1.588968e+02 | -127.4657712 | 0.8254984 | 0.1780880 | 0.0575578 | 0.0162793 | -0.0485039 | 0.0646074 | -0.0249464 | 0.0136247 | -0.0256496 | -0.0135192 | 0.0165430 | 0.0169825 | -0.0105657 | 0.0085088 | -0.0302380 | -0.0365493 |
| INFANT_FORMULAS | -0.0403115 | 0.0440737 | 0.0007208 | -0.0087198 | -0.0067860 | -0.0050280 | -3.4669667 | 0.0216413 | 0.0065399 | 0.0020745 | 0.0120601 | 0.0268275 | -0.0111986 | -0.0065399 | 0.0509124 | 0.0059949 | 0.0210084 | -4.107820e+01 | -31.1560072 | 0.1780880 | 0.2468268 | -0.0088429 | 0.0484863 | -0.0069794 | 0.0184065 | 0.0051686 | 0.0233466 | -0.0338244 | 0.0022503 | 0.0074364 | 0.0412609 | -0.0121304 | -0.0164200 | -0.0170353 | 0.0130445 |
| ADDITIONAL_FOOD_SWEETENING | 0.0467107 | -0.0134841 | 0.0060124 | 0.0188812 | 0.0533561 | -0.0081221 | 2.2320945 | -0.0251573 | -0.0036567 | 0.0204810 | 0.0563271 | 0.0953729 | 0.0239267 | 0.0120601 | 0.1400970 | 0.0022503 | 0.0672269 | 2.843624e+00 | 1.9344784 | 0.0575578 | -0.0088429 | 0.4954116 | 0.0159453 | -0.0219402 | 0.0025843 | -0.0142752 | 0.0173517 | -0.0252277 | -0.0642382 | 0.0310819 | -0.0025140 | -0.0451285 | 0.0665588 | 0.0332443 | 0.0340002 |
| CHILD_FLUORIDE_SUPPLEMENTS | 0.0702331 | -0.0416828 | -0.0164024 | 0.0099680 | -0.0467459 | 0.0113217 | 1.9909637 | -0.0241377 | 0.0166837 | 0.0102844 | -0.0521606 | -0.0593509 | 0.0691431 | 0.0253331 | -0.0323125 | 0.0065399 | 0.0294118 | -7.507384e+00 | -2.3336732 | 0.0162793 | 0.0484863 | 0.0159453 | 0.2752013 | 0.0622868 | 0.0399423 | 0.0052565 | -0.0043247 | 0.0150487 | 0.1223937 | -0.0262649 | 0.1015435 | 0.0585774 | -0.0878134 | -0.0369185 | -0.0075419 |
| CHILD_FLUORIDE_TOOTHPASTE | 0.2147076 | -0.0391688 | 0.0087550 | 0.0137829 | -0.0905207 | 0.0434584 | 0.2367533 | -0.0611793 | 0.0064695 | 0.0025491 | -0.1083823 | -0.1031258 | 0.0349144 | 0.0649590 | -0.1596287 | -0.0094758 | 0.0714286 | 2.040498e+01 | 9.5008614 | -0.0485039 | -0.0069794 | -0.0219402 | 0.0622868 | 0.4841954 | 0.0316269 | 0.1054112 | -0.0355473 | 0.0119897 | 0.0457790 | -0.0255793 | 0.0926831 | 0.0801660 | -0.0948103 | -0.0633417 | -0.0116733 |
| CHILD_ORAL_HYGIENE | 0.1758553 | -0.0178088 | -0.0012130 | 0.0247178 | -0.0782497 | 0.0710770 | -1.4686896 | -0.0536374 | 0.0591927 | 0.0783904 | -0.0998207 | -0.0740480 | 0.0110580 | 0.0626560 | -0.0879364 | -0.0112162 | 0.0000000 | -1.504928e+01 | -18.1670476 | 0.0646074 | 0.0184065 | 0.0025843 | 0.0399423 | 0.0316269 | 0.2583243 | 0.1817095 | -0.0374459 | -0.0157343 | 0.0823986 | -0.0388524 | 0.0735206 | 0.1057804 | -0.0895011 | -0.0573116 | -0.0319961 |
| CHILD_TOOTH_BRUSHING | 0.2232868 | -0.1661334 | -0.0485215 | 0.0223269 | -0.0585598 | 0.0951795 | -2.8484231 | -0.0866707 | 0.0483809 | 0.0893956 | -0.1188777 | -0.0837699 | 0.0431595 | 0.0692662 | -0.1309026 | 0.0177385 | 0.1470588 | 2.478100e+01 | 20.0365845 | -0.0249464 | 0.0051686 | -0.0142752 | 0.0052565 | 0.1054112 | 0.1817095 | 0.7557751 | -0.0608628 | 0.0428958 | 0.1698956 | -0.0288844 | 0.0710770 | 0.0841567 | -0.0800429 | -0.0655743 | -0.0319433 |
| DIARRHEA_DURING_INFANCY | -0.1511902 | 0.0130797 | 0.0056608 | -0.0243135 | 0.0514398 | -0.0221687 | -2.2147428 | 0.0234169 | -0.0521430 | -0.0408917 | 0.0890791 | 0.0766499 | -0.0277944 | -0.0444956 | 0.1442636 | 0.0201294 | -0.0420168 | -8.092542e-01 | 0.8831968 | 0.0136247 | 0.0233466 | 0.0173517 | -0.0043247 | -0.0355473 | -0.0374459 | -0.0608628 | 0.0907141 | -0.0204107 | -0.0680004 | 0.0188460 | -0.0321719 | -0.0832777 | 0.0479238 | 0.0461657 | 0.0162617 |
| MEDICAL_SYRUPS | 0.0760346 | -0.0340354 | -0.0199712 | -0.0148026 | 0.0121655 | -0.0675961 | -0.3912837 | -0.0040083 | 0.0005977 | -0.0313456 | -0.0287613 | 0.0037622 | 0.0303084 | 0.0036040 | -0.0381316 | -0.0113568 | -0.0042017 | 7.039309e-01 | 7.1224992 | -0.0256496 | -0.0338244 | -0.0252277 | 0.0150487 | 0.0119897 | -0.0157343 | 0.0428958 | -0.0204107 | 0.2618403 | -0.0044478 | -0.0176857 | -0.0022151 | 0.0138005 | -0.0038501 | -0.0517738 | -0.0335959 |
| CHILD_FIRST_DENTIST_VISIT | 0.2295805 | -0.1005415 | -0.0208678 | 0.0626560 | -0.0723427 | 0.0291481 | 1.0518442 | -0.0412784 | 0.1714954 | 0.1186667 | -0.1399916 | -0.1521747 | 0.0198481 | 0.0427903 | -0.2445238 | 0.0020921 | -0.0126050 | 2.745779e+01 | 10.4734538 | -0.0135192 | 0.0022503 | -0.0642382 | 0.1223937 | 0.0457790 | 0.0823986 | 0.1698956 | -0.0680004 | -0.0044478 | 0.8653704 | -0.0690552 | 0.0110228 | 0.0992933 | -0.1185964 | -0.0850005 | 0.0096164 |
| SWEETS_DURING_PREGNANCY | -0.0888330 | 0.0190746 | 0.0233114 | 0.0154882 | 0.0333497 | -0.0111459 | -5.4078795 | 0.0068387 | -0.0525825 | -0.0347737 | 0.0642558 | 0.0375514 | 0.0324707 | -0.0272494 | 0.0846841 | 0.0158750 | -0.0126050 | -1.217176e+01 | 0.9171970 | 0.0165430 | 0.0074364 | 0.0310819 | -0.0262649 | -0.0255793 | -0.0388524 | -0.0288844 | 0.0188460 | -0.0176857 | -0.0690552 | 0.2179600 | -0.0248585 | -0.0337365 | 0.0548328 | 0.0403643 | 0.0067332 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 0.0830315 | -0.0309237 | 0.0558876 | 0.0008790 | -0.0880595 | 0.0564502 | -4.8317218 | -0.0311698 | -0.0069618 | -0.0244366 | -0.0750501 | -0.0418410 | 0.1282128 | 0.0531803 | -0.0830667 | -0.0063816 | 0.0714286 | -4.396698e+00 | -6.2053022 | 0.0169825 | 0.0412609 | -0.0025140 | 0.1015435 | 0.0926831 | 0.0735206 | 0.0710770 | -0.0321719 | -0.0022151 | 0.0110228 | -0.0248585 | 0.6160121 | 0.0617067 | -0.1429978 | -0.0801308 | -0.0058894 |
| ORAL_HEALTH_DURING_PREGNANCY | 0.3362751 | -0.0452692 | -0.0096867 | 0.0354066 | -0.1114061 | 0.0018811 | 0.2974052 | -0.0414015 | 0.0973946 | 0.1156957 | -0.1705812 | -0.1366162 | 0.0914701 | 0.0916810 | -0.2072712 | -0.0311698 | 0.1176471 | 1.103695e+01 | -2.4352871 | -0.0105657 | -0.0121304 | -0.0451285 | 0.0585774 | 0.0801660 | 0.1057804 | 0.0841567 | -0.0832777 | 0.0138005 | 0.0992933 | -0.0337365 | 0.0617067 | 0.5309237 | -0.1268415 | -0.1219542 | -0.0431068 |
| MOTHER_HEALTH_AWARENESS | -0.2241307 | 0.0427903 | -0.0110052 | -0.0246827 | 0.0967441 | -0.0644492 | 2.7656728 | 0.0409620 | -0.1159418 | -0.1070989 | 0.1759783 | 0.1765761 | -0.0793748 | -0.0773355 | 0.2484793 | 0.0180198 | -0.0588235 | -1.529684e+01 | -9.5845259 | 0.0085088 | -0.0164200 | 0.0665588 | -0.0878134 | -0.0948103 | -0.0895011 | -0.0800429 | 0.0479238 | -0.0038501 | -0.1185964 | 0.0548328 | -0.1429978 | -0.1268415 | 0.2486551 | 0.1208291 | 0.0468865 |
| FATHER_HEALTH_AWARENESS | -0.3318449 | 0.0415597 | 0.0091769 | -0.0167364 | 0.0790057 | -0.0329630 | -10.2085546 | 0.0166661 | -0.0798847 | -0.0826272 | 0.1607187 | 0.1630393 | -0.0778102 | -0.0671741 | 0.2118421 | 0.0178088 | -0.0630252 | -2.070391e+00 | 3.2027355 | -0.0302380 | -0.0170353 | 0.0332443 | -0.0369185 | -0.0633417 | -0.0573116 | -0.0655743 | 0.0461657 | -0.0517738 | -0.0850005 | 0.0403643 | -0.0801308 | -0.1219542 | 0.1208291 | 0.3035055 | 0.0591927 |
| ECC | -0.1265427 | 0.0344925 | 0.0444077 | -0.0272846 | 0.0096691 | -0.0212897 | -6.4421785 | -0.0000527 | -0.0383601 | -0.0666995 | 0.0823283 | 0.0852994 | -0.0365318 | -0.0120601 | 0.0951971 | 0.0271615 | -0.0252101 | -2.902447e+00 | -1.9302767 | -0.0365493 | 0.0130445 | 0.0340002 | -0.0075419 | -0.0116733 | -0.0319961 | -0.0319433 | 0.0162617 | -0.0335959 | 0.0096164 | 0.0067332 | -0.0058894 | -0.0431068 | 0.0468865 | 0.0591927 | 0.2096973 |
kable(cor(NUM))| CHILD_ETHNICITY | CHILD_AGE | CHILD_GENDER | CHILD_SERBIAN_LANGUAGE | MOTHER_AGE | MARITAL_STATUS | MOTHER_ETHNICITY | MOTHER_SERBIAN_LANGUAGE | NUMBER_OF_CHILDREN | BIRTH_ORDER | MOTHER_EDUCATION_LEVEL | MOTHER_EMPLOYMENT_STATUS | QUALITY_OF_HOUSING | HOUSING_CONDITIONS | HOUSEHOLD_MONTHLY_INCOME | BIRTH_WEIGHT | BREASTFEEDING | BREASTFEEDING_FREQUENCY | BREASTFEEDING_DURING_NIGHT | BOTTLE_FEEDING | INFANT_FORMULAS | ADDITIONAL_FOOD_SWEETENING | CHILD_FLUORIDE_SUPPLEMENTS | CHILD_FLUORIDE_TOOTHPASTE | CHILD_ORAL_HYGIENE | CHILD_TOOTH_BRUSHING | DIARRHEA_DURING_INFANCY | MEDICAL_SYRUPS | CHILD_FIRST_DENTIST_VISIT | SWEETS_DURING_PREGNANCY | FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | ORAL_HEALTH_DURING_PREGNANCY | MOTHER_HEALTH_AWARENESS | FATHER_HEALTH_AWARENESS | ECC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CHILD_ETHNICITY | 1.0000000 | -0.1749489 | 0.0458069 | 0.3125799 | -0.2236191 | 0.0584724 | 0.2484739 | -0.2700453 | 0.2590974 | 0.2653392 | -0.5062151 | -0.3786649 | 0.0846922 | 0.4043858 | -0.4509273 | -0.1693861 | 0.1872540 | 0.0996975 | 0.0031334 | 0.0247279 | -0.0547197 | 0.0447553 | 0.0902875 | 0.2080885 | 0.2333370 | 0.1732119 | -0.3385299 | 0.1002083 | 0.1664351 | -0.1283208 | 0.0713443 | 0.3112358 | -0.3031192 | -0.4062213 | -0.1863595 |
| CHILD_AGE | -0.1749489 | 1.0000000 | 0.0682532 | -0.1128055 | 0.1843916 | 0.0252681 | 0.0860941 | 0.0400513 | -0.0134590 | -0.0554175 | 0.1337950 | 0.1173478 | -0.0615070 | -0.1092632 | 0.1735880 | 0.0530263 | -0.0837080 | -0.0441101 | -0.0344754 | -0.0428397 | 0.1136630 | -0.0245456 | -0.1018046 | -0.0721217 | -0.0448939 | -0.2448479 | 0.0556412 | -0.0852213 | -0.1384778 | 0.0523483 | -0.0504815 | -0.0796017 | 0.1099469 | 0.0966552 | 0.0965081 |
| CHILD_GENDER | 0.0458069 | 0.0682532 | 1.0000000 | -0.0770224 | -0.1768189 | 0.0310033 | -0.0794708 | -0.1465002 | 0.0149906 | -0.0387792 | -0.0616617 | -0.0119567 | 0.0930756 | 0.0739673 | 0.0135062 | -0.0173448 | -0.0652926 | -0.0844175 | -0.0985704 | 0.0811014 | 0.0028999 | 0.0170738 | -0.0624949 | 0.0251482 | -0.0047704 | -0.1115580 | 0.0375670 | -0.0780096 | -0.0448371 | 0.0998029 | 0.1423259 | -0.0265720 | -0.0441127 | 0.0332947 | 0.1938318 |
| CHILD_SERBIAN_LANGUAGE | 0.3125799 | -0.1128055 | -0.0770224 | 1.0000000 | -0.1814429 | 0.0303336 | -0.0536624 | -0.1506973 | 0.2155456 | 0.2105393 | -0.3156437 | -0.2095788 | 0.0520194 | 0.1547974 | -0.2379001 | -0.1298140 | 0.0000000 | -0.0285229 | -0.0402614 | 0.0046495 | -0.0514325 | 0.0786092 | 0.0556814 | 0.0580441 | 0.1425132 | 0.0752592 | -0.2365577 | -0.0847708 | 0.1973736 | 0.0972165 | 0.0032819 | 0.1423954 | -0.1450510 | -0.0890238 | -0.1746014 |
| MOTHER_AGE | -0.2236191 | 0.1843916 | -0.1768189 | -0.1814429 | 1.0000000 | -0.1289554 | 0.0322207 | 0.1874197 | 0.0557514 | 0.1258653 | 0.3549148 | 0.2798053 | -0.1904334 | -0.3326204 | 0.3433659 | 0.2685220 | -0.0508174 | 0.0007155 | 0.0323214 | -0.0097928 | -0.0188875 | 0.1048237 | -0.1232187 | -0.1798855 | -0.2128917 | -0.0931455 | 0.2361677 | 0.0328754 | -0.1075357 | 0.0987785 | -0.1551458 | -0.2114226 | 0.2682777 | 0.1983049 | 0.0291978 |
| MARITAL_STATUS | 0.0584724 | 0.0252681 | 0.0310033 | 0.0303336 | -0.1289554 | 1.0000000 | 0.0430172 | -0.2171557 | -0.0729327 | -0.0450615 | -0.1289093 | -0.0731570 | 0.0551055 | 0.2078917 | -0.1301317 | 0.0325620 | 0.0514674 | 0.0780334 | 0.0458139 | -0.0443525 | -0.0182229 | -0.0207782 | 0.0388605 | 0.1124570 | 0.2518079 | 0.1971379 | -0.1325336 | -0.2378627 | 0.0564198 | -0.0429882 | 0.1295072 | 0.0046485 | -0.2327245 | -0.1077373 | -0.0837136 |
| MOTHER_ETHNICITY | 0.2484739 | 0.0860941 | -0.0794708 | -0.0536624 | 0.0322207 | 0.0430172 | 1.0000000 | 0.0195604 | 0.0215248 | 0.0249630 | -0.0068414 | -0.0081914 | -0.0073500 | -0.0443807 | 0.0292392 | 0.0444335 | 0.0299848 | 0.0381773 | -0.0466430 | -0.0041086 | -0.0488039 | 0.0221784 | 0.0265423 | 0.0023795 | -0.0202092 | -0.0229144 | -0.0514265 | -0.0053478 | 0.0079077 | -0.0810102 | -0.0430535 | 0.0028545 | 0.0387885 | -0.1295931 | -0.0983869 |
| MOTHER_SERBIAN_LANGUAGE | -0.2700453 | 0.0400513 | -0.1465002 | -0.1506973 | 0.1874197 | -0.2171557 | 0.0195604 | 1.0000000 | -0.2186217 | -0.1291851 | 0.2324506 | 0.2453748 | -0.0904828 | -0.3009693 | 0.2272624 | 0.1342954 | -0.0184043 | 0.0439592 | 0.0291700 | -0.0488386 | 0.0981670 | -0.0805490 | -0.1036929 | -0.1981402 | -0.2378281 | -0.2246747 | 0.1752146 | -0.0176531 | -0.1000001 | 0.0330115 | -0.0894989 | -0.1280497 | 0.1851232 | 0.0681755 | -0.0002596 |
| NUMBER_OF_CHILDREN | 0.2590974 | -0.0134590 | 0.0149906 | 0.2155456 | 0.0557514 | -0.0729327 | 0.0215248 | -0.2186217 | 1.0000000 | 0.8100678 | -0.2958563 | -0.3345987 | 0.0885621 | 0.3032983 | -0.3498081 | -0.1193109 | -0.0540986 | -0.0376300 | -0.0537415 | 0.0443750 | 0.0174400 | -0.0068830 | 0.0421348 | 0.0123179 | 0.1542980 | 0.0737313 | -0.2293684 | 0.0015476 | 0.2442452 | -0.1492203 | -0.0117517 | 0.1770898 | -0.3080463 | -0.1921122 | -0.1109834 |
| BIRTH_ORDER | 0.2653392 | -0.0554175 | -0.0387792 | 0.2105393 | 0.1258653 | -0.0450615 | 0.0249630 | -0.1291851 | 0.8100678 | 1.0000000 | -0.2913549 | -0.3080443 | 0.0620992 | 0.2819634 | -0.3125075 | 0.0169959 | -0.0052460 | -0.0162645 | -0.0356915 | 0.0229449 | 0.0053645 | 0.0373840 | 0.0251868 | 0.0047065 | 0.1981514 | 0.1321104 | -0.1744274 | -0.0787001 | 0.1638872 | -0.0956930 | -0.0400002 | 0.2039944 | -0.2759329 | -0.1926890 | -0.1871299 |
| MOTHER_EDUCATION_LEVEL | -0.5062151 | 0.1337950 | -0.0616617 | -0.3156437 | 0.3549148 | -0.1289093 | -0.0068414 | 0.2324506 | -0.2958563 | -0.2913549 | 1.0000000 | 0.5153962 | -0.2048867 | -0.5012175 | 0.6296877 | 0.2192816 | -0.0788070 | -0.0435909 | -0.0038845 | 0.0559298 | 0.0360382 | 0.1188078 | -0.1476141 | -0.2312374 | -0.2915736 | -0.2030085 | 0.4390853 | -0.0834450 | -0.2234144 | 0.2043311 | -0.1419603 | -0.3475565 | 0.5239270 | 0.4331051 | 0.2669090 |
| MOTHER_EMPLOYMENT_STATUS | -0.3786649 | 0.1173478 | -0.0119567 | -0.2095788 | 0.2798053 | -0.0731570 | -0.0081914 | 0.2453748 | -0.3345987 | -0.3080443 | 0.5153962 | 1.0000000 | -0.1653301 | -0.4251562 | 0.5518383 | 0.2592017 | 0.0142261 | 0.0308273 | 0.0607014 | 0.0562224 | 0.0627102 | 0.1573608 | -0.1313884 | -0.1721122 | -0.1691943 | -0.1119042 | 0.2955486 | 0.0085384 | -0.1899748 | 0.0934099 | -0.0619102 | -0.2177413 | 0.4112329 | 0.3436875 | 0.2163238 |
| QUALITY_OF_HOUSING | 0.0846922 | -0.0615070 | 0.0930756 | 0.0520194 | -0.1904334 | 0.0551055 | -0.0073500 | -0.0904828 | 0.0885621 | 0.0620992 | -0.2048867 | -0.1653301 | 1.0000000 | 0.1792047 | -0.2062449 | -0.1012751 | 0.0587079 | -0.0256308 | -0.0429956 | -0.0595128 | -0.0249293 | 0.0375961 | 0.1457693 | 0.0554928 | 0.0240622 | 0.0549064 | -0.1020615 | 0.0655068 | 0.0235972 | 0.0769212 | 0.1806671 | 0.1388370 | -0.1760461 | -0.1562052 | -0.0882301 |
| HOUSING_CONDITIONS | 0.4043858 | -0.1092632 | 0.0739673 | 0.1547974 | -0.3326204 | 0.2078917 | -0.0443807 | -0.3009693 | 0.3032983 | 0.2819634 | -0.5012175 | -0.4251562 | 0.1792047 | 1.0000000 | -0.4128096 | -0.1825417 | 0.1355732 | 0.0082770 | -0.0581606 | 0.0269823 | -0.0437053 | 0.0568891 | 0.1603343 | 0.3099502 | 0.4093010 | 0.2645377 | -0.4905039 | 0.0233842 | 0.1527240 | -0.1937899 | 0.2249668 | 0.4177592 | -0.5149238 | -0.4048382 | -0.0874411 |
| HOUSEHOLD_MONTHLY_INCOME | -0.4509273 | 0.1735880 | 0.0135062 | -0.2379001 | 0.3433659 | -0.1301317 | 0.0292392 | 0.2272624 | -0.3498081 | -0.3125075 | 0.6296877 | 0.5518383 | -0.2062449 | -0.4128096 | 1.0000000 | 0.3137724 | -0.0523160 | -0.0685241 | -0.0217838 | 0.0743900 | 0.0937827 | 0.1821549 | -0.0563690 | -0.2099402 | -0.1583366 | -0.1377993 | 0.4383431 | -0.0681964 | -0.2405553 | 0.1660001 | -0.0968562 | -0.2603262 | 0.4560228 | 0.3519037 | 0.1902489 |
| BIRTH_WEIGHT | -0.1693861 | 0.0530263 | -0.0173448 | -0.1298140 | 0.2685220 | 0.0325620 | 0.0444335 | 0.1342954 | -0.1193109 | 0.0169959 | 0.2192816 | 0.2592017 | -0.1012751 | -0.1825417 | 0.3137724 | 1.0000000 | 0.0000000 | -0.0191671 | 0.0012635 | 0.0221744 | 0.0416514 | 0.0110357 | 0.0430318 | -0.0470056 | -0.0761745 | 0.0704314 | 0.2306957 | -0.0766100 | 0.0077628 | 0.1173737 | -0.0280662 | -0.1476604 | 0.1247374 | 0.1115829 | 0.2047404 |
| BREASTFEEDING | 0.1872540 | -0.0837080 | -0.0652926 | 0.0000000 | -0.0508174 | 0.0514674 | 0.0299848 | -0.0184043 | -0.0540986 | -0.0052460 | -0.0788070 | 0.0142261 | 0.0587079 | 0.1355732 | -0.0523160 | 0.0000000 | 1.0000000 | 0.6716940 | 0.6202096 | -0.1842625 | 0.0410946 | 0.0928214 | 0.0544859 | 0.0997585 | 0.0000000 | 0.1643929 | -0.1355732 | -0.0079798 | -0.0131684 | -0.0262388 | 0.0884434 | 0.1569109 | -0.1146412 | -0.1111781 | -0.0535015 |
| BREASTFEEDING_FREQUENCY | 0.0996975 | -0.0441101 | -0.0844175 | -0.0285229 | 0.0007155 | 0.0780334 | 0.0381773 | 0.0439592 | -0.0376300 | -0.0162645 | -0.0435909 | 0.0308273 | -0.0256308 | 0.0082770 | -0.0685241 | -0.0191671 | 0.6716940 | 1.0000000 | 0.8742465 | -0.5443940 | -0.2573781 | 0.0125761 | -0.0445471 | 0.0912814 | -0.0921700 | 0.0887317 | -0.0083638 | 0.0042822 | 0.0918800 | -0.0811561 | -0.0174377 | 0.0471508 | -0.0954903 | -0.0116984 | -0.0197299 |
| BREASTFEEDING_DURING_NIGHT | 0.0031334 | -0.0344754 | -0.0985704 | -0.0402614 | 0.0323214 | 0.0458139 | -0.0466430 | 0.0291700 | -0.0537415 | -0.0356915 | -0.0038845 | 0.0607014 | -0.0429956 | -0.0581606 | -0.0217838 | 0.0012635 | 0.6202096 | 0.8742465 | 1.0000000 | -0.4853097 | -0.2169348 | 0.0095075 | -0.0153886 | 0.0472320 | -0.1236475 | 0.0797280 | 0.0101439 | 0.0481502 | 0.0389469 | 0.0067961 | -0.0273497 | -0.0115616 | -0.0664899 | 0.0201104 | -0.0145817 |
| BOTTLE_FEEDING | 0.0247279 | -0.0428397 | 0.0811014 | 0.0046495 | -0.0097928 | -0.0443525 | -0.0041086 | -0.0488386 | 0.0443750 | 0.0229449 | 0.0559298 | 0.0562224 | -0.0595128 | 0.0269823 | 0.0743900 | 0.0221744 | -0.1842625 | -0.5443940 | -0.4853097 | 1.0000000 | 0.3945303 | 0.0900042 | 0.0341549 | -0.0767200 | 0.1399078 | -0.0315830 | 0.0497888 | -0.0551701 | -0.0159953 | 0.0390003 | 0.0238149 | -0.0159597 | 0.0187808 | -0.0604105 | -0.0878466 |
| INFANT_FORMULAS | -0.0547197 | 0.1136630 | 0.0028999 | -0.0514325 | -0.0188875 | -0.0182229 | -0.0488039 | 0.0981670 | 0.0174400 | 0.0053645 | 0.0360382 | 0.0627102 | -0.0249293 | -0.0437053 | 0.0937827 | 0.0416514 | 0.0410946 | -0.2573781 | -0.2169348 | 0.3945303 | 1.0000000 | -0.0252880 | 0.1860364 | -0.0201887 | 0.0728942 | 0.0119669 | 0.1560234 | -0.1330503 | 0.0048690 | 0.0320613 | 0.1058151 | -0.0335090 | -0.0662792 | -0.0622400 | 0.0573372 |
| ADDITIONAL_FOOD_SWEETENING | 0.0447553 | -0.0245456 | 0.0170738 | 0.0786092 | 0.1048237 | -0.0207782 | 0.0221784 | -0.0805490 | -0.0068830 | 0.0373840 | 0.1188078 | 0.1573608 | 0.0375961 | 0.0568891 | 0.1821549 | 0.0110357 | 0.0928214 | 0.0125761 | 0.0095075 | 0.0900042 | -0.0252880 | 1.0000000 | 0.0431841 | -0.0447967 | 0.0072240 | -0.0233293 | 0.0818506 | -0.0700448 | -0.0981092 | 0.0945880 | -0.0045508 | -0.0879938 | 0.1896374 | 0.0857335 | 0.1054878 |
| CHILD_FLUORIDE_SUPPLEMENTS | 0.0902875 | -0.1018046 | -0.0624949 | 0.0556814 | -0.1232187 | 0.0388605 | 0.0265423 | -0.1036929 | 0.0421348 | 0.0251868 | -0.1476141 | -0.1313884 | 0.1457693 | 0.1603343 | -0.0563690 | 0.0430318 | 0.0544859 | -0.0445471 | -0.0153886 | 0.0341549 | 0.1860364 | 0.0431841 | 1.0000000 | 0.1706321 | 0.1498048 | 0.0115259 | -0.0273714 | 0.0560603 | 0.2508031 | -0.1072413 | 0.2466224 | 0.1532459 | -0.3356887 | -0.1277426 | -0.0313950 |
| CHILD_FLUORIDE_TOOTHPASTE | 0.2080885 | -0.0721217 | 0.0251482 | 0.0580441 | -0.1798855 | 0.1124570 | 0.0023795 | -0.1981402 | 0.0123179 | 0.0047065 | -0.2312374 | -0.1721122 | 0.0554928 | 0.3099502 | -0.2099402 | -0.0470056 | 0.0997585 | 0.0912814 | 0.0472320 | -0.0767200 | -0.0201887 | -0.0447967 | 0.1706321 | 1.0000000 | 0.0894259 | 0.1742530 | -0.1696128 | 0.0336729 | 0.0707220 | -0.0787389 | 0.1697054 | 0.1581116 | -0.2732414 | -0.1652326 | -0.0366342 |
| CHILD_ORAL_HYGIENE | 0.2333370 | -0.0448939 | -0.0047704 | 0.1425132 | -0.2128917 | 0.2518079 | -0.0202092 | -0.2378281 | 0.1542980 | 0.1981514 | -0.2915736 | -0.1691943 | 0.0240622 | 0.4093010 | -0.1583366 | -0.0761745 | 0.0000000 | -0.0921700 | -0.1236475 | 0.1399078 | 0.0728942 | 0.0072240 | 0.1498048 | 0.0894259 | 1.0000000 | 0.4112432 | -0.2446159 | -0.0604989 | 0.1742756 | -0.1637369 | 0.1843028 | 0.2856318 | -0.3531400 | -0.2046807 | -0.1374730 |
| CHILD_TOOTH_BRUSHING | 0.1732119 | -0.2448479 | -0.1115580 | 0.0752592 | -0.0931455 | 0.1971379 | -0.0229144 | -0.2246747 | 0.0737313 | 0.1321104 | -0.2030085 | -0.1119042 | 0.0549064 | 0.2645377 | -0.1377993 | 0.0704314 | 0.1643929 | 0.0887317 | 0.0797280 | -0.0315830 | 0.0119669 | -0.0233293 | 0.0115259 | 0.1742530 | 0.4112432 | 1.0000000 | -0.2324441 | 0.0964274 | 0.2100800 | -0.0711669 | 0.1041689 | 0.1328545 | -0.1846409 | -0.1369161 | -0.0802393 |
| DIARRHEA_DURING_INFANCY | -0.3385299 | 0.0556412 | 0.0375670 | -0.2365577 | 0.2361677 | -0.1325336 | -0.0514265 | 0.1752146 | -0.2293684 | -0.1744274 | 0.4390853 | 0.2955486 | -0.1020615 | -0.4905039 | 0.4383431 | 0.2306957 | -0.1355732 | -0.0083638 | 0.0101439 | 0.0497888 | 0.1560234 | 0.0818506 | -0.0273714 | -0.1696128 | -0.2446159 | -0.2324441 | 1.0000000 | -0.1324347 | -0.2427019 | 0.1340276 | -0.1360956 | -0.3794679 | 0.3190912 | 0.2782269 | 0.1179052 |
| MEDICAL_SYRUPS | 0.1002083 | -0.0852213 | -0.0780096 | -0.0847708 | 0.0328754 | -0.2378627 | -0.0053478 | -0.0176531 | 0.0015476 | -0.0787001 | -0.0834450 | 0.0085384 | 0.0655068 | 0.0233842 | -0.0681964 | -0.0766100 | -0.0079798 | 0.0042822 | 0.0481502 | -0.0551701 | -0.1330503 | -0.0700448 | 0.0560603 | 0.0336729 | -0.0604989 | 0.0964274 | -0.1324347 | 1.0000000 | -0.0093439 | -0.0740315 | -0.0055155 | 0.0370135 | -0.0150887 | -0.1836576 | -0.1433743 |
| CHILD_FIRST_DENTIST_VISIT | 0.1664351 | -0.1384778 | -0.0448371 | 0.1973736 | -0.1075357 | 0.0564198 | 0.0079077 | -0.1000001 | 0.2442452 | 0.1638872 | -0.2234144 | -0.1899748 | 0.0235972 | 0.1527240 | -0.2405553 | 0.0077628 | -0.0131684 | 0.0918800 | 0.0389469 | -0.0159953 | 0.0048690 | -0.0981092 | 0.2508031 | 0.0707220 | 0.1742756 | 0.2100800 | -0.2427019 | -0.0093439 | 1.0000000 | -0.1590037 | 0.0150972 | 0.1464882 | -0.2556653 | -0.1658583 | 0.0225743 |
| SWEETS_DURING_PREGNANCY | -0.1283208 | 0.0523483 | 0.0998029 | 0.0972165 | 0.0987785 | -0.0429882 | -0.0810102 | 0.0330115 | -0.1492203 | -0.0956930 | 0.2043311 | 0.0934099 | 0.0769212 | -0.1937899 | 0.1660001 | 0.1173737 | -0.0262388 | -0.0811561 | 0.0067961 | 0.0390003 | 0.0320613 | 0.0945880 | -0.1072413 | -0.0787389 | -0.1637369 | -0.0711669 | 0.1340276 | -0.0740315 | -0.1590037 | 1.0000000 | -0.0678409 | -0.0991735 | 0.2355339 | 0.1569370 | 0.0314948 |
| FLUORIDE_SUPPLEMENTS_DURING_PREGNANCY | 0.0713443 | -0.0504815 | 0.1423259 | 0.0032819 | -0.1551458 | 0.1295072 | -0.0430535 | -0.0894989 | -0.0117517 | -0.0400002 | -0.1419603 | -0.0619102 | 0.1806671 | 0.2249668 | -0.0968562 | -0.0280662 | 0.0884434 | -0.0174377 | -0.0273497 | 0.0238149 | 0.1058151 | -0.0045508 | 0.2466224 | 0.1697054 | 0.1843028 | 0.1041689 | -0.1360956 | -0.0055155 | 0.0150972 | -0.0678409 | 1.0000000 | 0.1079000 | -0.3653726 | -0.1853197 | -0.0163862 |
| ORAL_HEALTH_DURING_PREGNANCY | 0.3112358 | -0.0796017 | -0.0265720 | 0.1423954 | -0.2114226 | 0.0046485 | 0.0028545 | -0.1280497 | 0.1770898 | 0.2039944 | -0.3475565 | -0.2177413 | 0.1388370 | 0.4177592 | -0.2603262 | -0.1476604 | 0.1569109 | 0.0471508 | -0.0115616 | -0.0159597 | -0.0335090 | -0.0879938 | 0.1532459 | 0.1581116 | 0.2856318 | 0.1328545 | -0.3794679 | 0.0370135 | 0.1464882 | -0.0991735 | 0.1079000 | 1.0000000 | -0.3490975 | -0.3038068 | -0.1291913 |
| MOTHER_HEALTH_AWARENESS | -0.3031192 | 0.1099469 | -0.0441127 | -0.1450510 | 0.2682777 | -0.2327245 | 0.0387885 | 0.1851232 | -0.3080463 | -0.2759329 | 0.5239270 | 0.4112329 | -0.1760461 | -0.5149238 | 0.4560228 | 0.1247374 | -0.1146412 | -0.0954903 | -0.0664899 | 0.0187808 | -0.0662792 | 0.1896374 | -0.3356887 | -0.2732414 | -0.3531400 | -0.1846409 | 0.3190912 | -0.0150887 | -0.2556653 | 0.2355339 | -0.3653726 | -0.3490975 | 1.0000000 | 0.4398347 | 0.2053303 |
| FATHER_HEALTH_AWARENESS | -0.4062213 | 0.0966552 | 0.0332947 | -0.0890238 | 0.1983049 | -0.1077373 | -0.1295931 | 0.0681755 | -0.1921122 | -0.1926890 | 0.4331051 | 0.3436875 | -0.1562052 | -0.4048382 | 0.3519037 | 0.1115829 | -0.1111781 | -0.0116984 | 0.0201104 | -0.0604105 | -0.0622400 | 0.0857335 | -0.1277426 | -0.1652326 | -0.2046807 | -0.1369161 | 0.2782269 | -0.1836576 | -0.1658583 | 0.1569370 | -0.1853197 | -0.3038068 | 0.4398347 | 1.0000000 | 0.2346327 |
| ECC | -0.1863595 | 0.0965081 | 0.1938318 | -0.1746014 | 0.0291978 | -0.0837136 | -0.0983869 | -0.0002596 | -0.1109834 | -0.1871299 | 0.2669090 | 0.2163238 | -0.0882301 | -0.0874411 | 0.1902489 | 0.2047404 | -0.0535015 | -0.0197299 | -0.0145817 | -0.0878466 | 0.0573372 | 0.1054878 | -0.0313950 | -0.0366342 | -0.1374730 | -0.0802393 | 0.1179052 | -0.1433743 | 0.0225743 | 0.0314948 | -0.0163862 | -0.1291913 | 0.2053303 | 0.2346327 | 1.0000000 |
- Box Plots Dealing with Outliers
To be able to have an idea about the outliers, we should plot boxplots of the numerical attributes.
for (col in 2:ncol(TRAIN)) {
boxplot(TRAIN[,col],main=paste("Boxplot of the",colnames(TRAIN)[col] ))
}
- All the boxplots were observed to have an opinion the outliers and their affects on the dataset analysis.
3. Classification Methods Implemented:
library(ade4)
library(data.table)
#COMBINE ALL DATA TO HAVE CONSISTENT
ALL_DATA <- rbind(TRAIN, VALIDATION, TEST)
ALL_DATA_x <- ALL_DATA[,1:35]
ALL_DATA_y <- ALL_DATA[36]
#APPLY ONE HOT METHOD TO CATEGORICAL AND NULL(999) INVOLVING FEATURES
col_names <- c("CITY", "CHILD_ETHNICITY", "MOTHER_ETHNICITY", "BREASTFEEDING_FREQUENCY", "BREASTFEEDING_DURING_NIGHT", "MOTHER_EMPLOYMENT_STATUS")
for (f in col_names){
df_all_dummy = acm.disjonctif(ALL_DATA_x[f])
ALL_DATA_x[f] = NULL
ALL_DATA_x = cbind(ALL_DATA_x, df_all_dummy)
}
#DELETE .999 FEATURES
col_names999 <- c("MOTHER_ETHNICITY.999", "BREASTFEEDING_FREQUENCY.999", "BREASTFEEDING_DURING_NIGHT.999")
for (f in col_names999){
ALL_DATA_x[f] = NULL
}
#NORMALIZATION FUNCTION
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
#APPLY NORMALIZATION
ALL_DATA_x <- as.data.frame(lapply(ALL_DATA_x, normalize))3.1. Association Rule Mining (implemented on the paper)
col_names <- colnames(TRAIN)
TRAIN_factor <- as.data.frame(lapply(TRAIN[,col_names], factor))
rules1 <- apriori(TRAIN_factor, appearance = list(rhs=c("ECC=1"), default="lhs"), parameter = list(minlen=2, maxlen=7, sup = 0.1, conf = 0.4, target="rules"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.4 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 7 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 23
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[125 item(s), 239 transaction(s)] done [0.00s].
## sorting and recoding items ... [93 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7## Warning in apriori(TRAIN_factor, appearance = list(rhs = c("ECC=1"),
## default = "lhs"), : Mining stopped (maxlen reached). Only patterns up to a
## length of 7 returned!## done [1.50s].
## writing ... [125 rule(s)] done [0.08s].
## creating S4 object ... done [0.10s].rules1<-sort(rules1, decreasing=TRUE, by="confidence")
#inspect(rules1)
rules2 <- apriori(TRAIN_factor, appearance = list(rhs=c("ECC=2"), default="lhs"), parameter = list(minlen=2, maxlen=7, sup = 0.3, conf = 0.8, target="rules"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.3 2
## maxlen target ext
## 7 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 71
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[125 item(s), 239 transaction(s)] done [0.00s].
## sorting and recoding items ... [47 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7## Warning in apriori(TRAIN_factor, appearance = list(rhs = c("ECC=2"),
## default = "lhs"), : Mining stopped (maxlen reached). Only patterns up to a
## length of 7 returned!## done [0.03s].
## writing ... [246 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules2<-sort(rules2, decreasing=TRUE, by="confidence")
#inspect(rules2)Justification for Model Parameters
3.2. SVM
#SEPARATE TRAIN, VALIDATION AND TEST
TRAIN_conv_x <- ALL_DATA_x[1:239,]
VALIDATION_conv_x <- ALL_DATA_x[240:273,]
TEST_conv_x <- ALL_DATA_x[274:341,]
TRAIN_y <- TRAIN[,36]
TRAIN_y <- as.factor(TRAIN_y)
VALIDATION_y <- VALIDATION[,36]
VALIDATION_y <- as.factor(VALIDATION_y)
TEST_y <- TEST[,36]
TEST_y <- as.factor(TEST_y)
#POSSIBLE COST AND GAMMA VALUES
cost_try = c(0.1, 0.5, 1, 5, 10, 20, 50, 80, 100, 500)
gamma_try = c(0.005, 0.01, 0.02, 0.05, 0.1, 0.5, 1, 2, 5, 10)
#BEST COST AND GAMMA VALUES SELECTED ACCORDING TO ACCURACY
max_accur = 0
best_cost = 1
best_gamma = 1
for (i in 1:10)
{
for (j in 1:10)
{
svm_model <- svm(x = TRAIN_conv_x, y = TRAIN_y, gamma = gamma_try[j], cost = cost_try[i])
svm_res <- predict(svm_model, VALIDATION_conv_x)
conf_res <- confusionMatrix(svm_res, VALIDATION_y)
if (max_accur < conf_res$overall[1])
{
max_accur = conf_res$overall[1]
best_cost = cost_try[i]
best_gamma = gamma_try[j]
print(conf_res$overall[1])
}
}
}## Accuracy
## 0.6764706
## Accuracy
## 0.7058824
## Accuracy
## 0.7647059#BEST VALUES PRINTED
print(best_cost)## [1] 5print(best_gamma)## [1] 0.01#TEST DATASET IS PREDICTED AND RESULTS ARE DISPLAYED
svm_model <- svm(x = TRAIN_conv_x, y = TRAIN_y, gamma = best_gamma, cost = best_cost)
svm_res <- predict(svm_model, TEST_conv_x)
conf_res <- confusionMatrix(svm_res, TEST_y)
print(conf_res)## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 5 2
## 2 17 44
##
## Accuracy : 0.7206
## 95% CI : (0.5985, 0.8227)
## No Information Rate : 0.6765
## P-Value [Acc > NIR] : 0.261543
##
## Kappa : 0.2236
## Mcnemar's Test P-Value : 0.001319
##
## Sensitivity : 0.22727
## Specificity : 0.95652
## Pos Pred Value : 0.71429
## Neg Pred Value : 0.72131
## Prevalence : 0.32353
## Detection Rate : 0.07353
## Detection Prevalence : 0.10294
## Balanced Accuracy : 0.59190
##
## 'Positive' Class : 1
## Justification for Model Parameters
3.3. KNN
#SEPARATE TRAIN, VALIDATION AND TEST
TRAIN_conv_x <- ALL_DATA_x[1:239,]
VALIDATION_conv_x <- ALL_DATA_x[240:273,]
TEST_conv_x <- ALL_DATA_x[274:341,]
TRAIN_y <- TRAIN[,36]
TRAIN_y <- as.factor(TRAIN_y)
VALIDATION_y <- VALIDATION[,36]
VALIDATION_y <- as.factor(VALIDATION_y)
TEST_y <- TEST[,36]
TEST_y <- as.factor(TEST_y)
#BEST K VALUE IS SELECTED ACCORDING TO ACCURACY
max_accur = 0
best_k_val = 1
for (i in 1:100)
{
test_pred <- knn(train = TRAIN_conv_x, test = VALIDATION_conv_x, cl = TRAIN_y, k=i)
conf_res <- confusionMatrix(test_pred, VALIDATION_y)
if (max_accur < conf_res$overall[1])
{
max_accur = conf_res$overall[1]
best_k_val = i
print(conf_res$overall[1])
}
}## Accuracy
## 0.7058824#BEST VALUES PRINTED
print(best_k_val)## [1] 1#TEST DATASET IS PREDICTED AND RESULTS ARE DISPLAYED
test_pred <- knn(train = TRAIN_conv_x, test = TEST_conv_x, cl = TRAIN_y, k=best_k_val)
conf_res <- confusionMatrix(test_pred, TEST_y)
print(conf_res)## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 8 7
## 2 14 39
##
## Accuracy : 0.6912
## 95% CI : (0.5674, 0.7976)
## No Information Rate : 0.6765
## P-Value [Acc > NIR] : 0.4545
##
## Kappa : 0.2306
## Mcnemar's Test P-Value : 0.1904
##
## Sensitivity : 0.3636
## Specificity : 0.8478
## Pos Pred Value : 0.5333
## Neg Pred Value : 0.7358
## Prevalence : 0.3235
## Detection Rate : 0.1176
## Detection Prevalence : 0.2206
## Balanced Accuracy : 0.6057
##
## 'Positive' Class : 1
## Justification for Model Parameters
3.4. Naive Bayesian
#SEPARATE TEST
TEST_conv_x <- ALL_DATA_x[274:341,]
TEST_y <- TEST[,36]
TEST_y <- as.factor(TEST_y)
#VALIDATION COMBINED WITH TRAIN
TV_conv_x <- ALL_DATA_x[1:273,]
TV_y <- c(TRAIN_y, VALIDATION_y)
TV_y <- as.factor(TV_y)
#BECAUSE OF NO PARAMETER SELECTION, NB APPLIED DIRECTLY
nb_model <- naiveBayes(x = TV_conv_x, y = TV_y, laplace = laplace)
nb_res <- predict(nb_model, TEST_conv_x)
conf_res <- confusionMatrix(nb_res, TEST_y)
print(conf_res)## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 15 28
## 2 7 18
##
## Accuracy : 0.4853
## 95% CI : (0.3622, 0.6097)
## No Information Rate : 0.6765
## P-Value [Acc > NIR] : 0.9996453
##
## Kappa : 0.0585
## Mcnemar's Test P-Value : 0.0007232
##
## Sensitivity : 0.6818
## Specificity : 0.3913
## Pos Pred Value : 0.3488
## Neg Pred Value : 0.7200
## Prevalence : 0.3235
## Detection Rate : 0.2206
## Detection Prevalence : 0.6324
## Balanced Accuracy : 0.5366
##
## 'Positive' Class : 1
## Justification for Model Parameters
3.5. Random Forest
#SEPARATE TRAIN, VALIDATION AND TEST
TRAIN_conv_x <- ALL_DATA_x[1:239,]
VALIDATION_conv_x <- ALL_DATA_x[240:273,]
TEST_conv_x <- ALL_DATA_x[274:341,]
TRAIN_y <- TRAIN[,36]
TRAIN_y <- as.factor(TRAIN_y)
VALIDATION_y <- VALIDATION[,36]
VALIDATION_y <- as.factor(VALIDATION_y)
TEST_y <- TEST[,36]
TEST_y <- as.factor(TEST_y)
#BEST NTREE VALUE IS SELECTED ACCORDING TO ACCURACY
max_accur = 0
res_num_of_tree = 0
num_of_tree = 16
for (i in 1:7)
{
set.seed(97)
rf_model <- randomForest(x = TRAIN_conv_x, y = TRAIN_y, ntree = num_of_tree)
rf_res <- predict(rf_model, VALIDATION_conv_x)
rf_res_round <- as.factor(round(as.numeric(rf_res)))
conf_res <- confusionMatrix(rf_res_round, VALIDATION_y)
if (conf_res$overall[1] > max_accur)
{
max_accur = conf_res$overall[1]
res_num_of_tree = num_of_tree
print(conf_res$overall[1])
}
num_of_tree = num_of_tree*2
}## Accuracy
## 0.5882353
## Accuracy
## 0.6470588
## Accuracy
## 0.6764706#BEST VALUES PRINTED
print(res_num_of_tree)## [1] 64#TEST DATASET IS PREDICTED AND RESULTS ARE DISPLAYED
set.seed(97)
rf_res_model <- randomForest(x = TRAIN_conv_x, y = TRAIN_y, ntree = res_num_of_tree)
rf_res <- predict(rf_model, TEST_conv_x)
rf_res_round <- as.factor(round(as.numeric(rf_res)))
conf_res <- confusionMatrix(rf_res_round, TEST_y)
print(conf_res)## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 4 2
## 2 18 44
##
## Accuracy : 0.7059
## 95% CI : (0.5829, 0.8102)
## No Information Rate : 0.6765
## P-Value [Acc > NIR] : 0.3537252
##
## Kappa : 0.1707
## Mcnemar's Test P-Value : 0.0007962
##
## Sensitivity : 0.18182
## Specificity : 0.95652
## Pos Pred Value : 0.66667
## Neg Pred Value : 0.70968
## Prevalence : 0.32353
## Detection Rate : 0.05882
## Detection Prevalence : 0.08824
## Balanced Accuracy : 0.56917
##
## 'Positive' Class : 1
## Justification for Model Parameters
3.6. ANN
Justification for Model Parameters
4. Clustering Methods
4.1. K-means
# K-means on training Data
X = ALL_DATA_x
# Using the elbow method to find optimal number of clusters
# Applying k-means to the dataset
set.seed(13)
kmeans = kmeans(X, 10, iter.max = 500)
# Visualizing library
# install.packages("cluster")
library(cluster)
clusplot(X,
kmeans$cluster,
lines = 0, # no line wanted
shade = TRUE, # shade depending on the denstiy
color = TRUE,
labels = 0,
plotchar = FALSE,
span = TRUE,
main = paste("Clusters of Data"),
xlab = "x-axis",
ylab = "y-axis")
Justification for K-means Parameters
Initial configuration is fixed. We will run k-means for k = 1:10. vi. Plot error vs k to find optimal number of clusters by using the elbow method.
set.seed(123)
wcss = vector() # an empty vector
for (i in 1:50) wcss[i] = sum(kmeans(X, i)$withinss)
plot(1:50, wcss, type = "b", main = paste("Clusters"), xlab = "# Clusters", ylab = "Within Cluster SS")
4.2. Hierarchical Clustering
In this section, we also apply hiearchical clustering. In order to understand with linkages work best for the well seperated data, we plot their dendrogram in a for loop.
As seen from the dendrograms, the best seperation is obtained when warD is used.
# 2.1. H-clust with different linkages
X = ALL_DATA_x
dend = list(list(),list(),list())
meth = c("ward.D", "single", "average")
names(dend) = meth
# Using dendrogram to find the opt num of clusters
for (i in 1:3) {
dend[i] = list(hclust(dist(X, method = "euclidean"), method = meth[i])) #dist.method: euc #agglom.method: ward
plot(dend[[i]],
main = paste("Dendrogram using", meth[i], sep = " " ), # title
xlab = "Points",
ylab = paste("Euclidean", "Distance", sep = " ")
)
}
# Fitting hierarchical clustering to the mall dataset with k = 4 (found using dendrogram)
numClus = 5
hc = hclust(dist(X, method = "euclidean"), method = "ward.D") # same function with different var.name
y_hc = cutree(hc, k = numClus) # cut tree where num.groups is 4
# Visualizing the clusters
# install.packages("cluster")
library(cluster)
clusplot(X[1:2],
y_hc,
lines = 0, # cluster merkezleri arasi ?izgi
shade = TRUE,
color = TRUE,
labels = 1, # 1: labellanacak noktalari secip goster 2: hepsini goster
plotchar = FALSE,
span = TRUE, # cluster icini tarama
main = paste("Clusters of Well Seperated Data using ward.D"),
xlab = "X1",
ylab = "X2")
clus_size = c(0,0,0)
for (i in 1:length(y_hc)) clus_size[y_hc[i]] = clus_size[y_hc[i]]+1
show(clus_size)## [1] 210 45 34 NA NAJustification for H-clustering Parameters
For H-clustering parameters, we first plot the dendogram of the clusters. On this dendogram, we see the separation distance (length) of the linkages. Then, we find the cluster numbers by cutting the tree at maximum length point.as Fitting hierarchical clustering to the mall dataset with k = 5 (found using dendrogram)
5. Comparison for Classification Models
6. Comparison for Clustering Models
Now, we compare our clustering models using wcss analysis. wcss is a vector of within-cluster sum of squares, one component per cluster. To do this, we begin with an empy wcss vectors and we calculate and sum within ss values of clusters by running the model with 100 different initial configurations.. We can view the sum of within cluster sum of squares error and look at indices with minimum error.
wcss_k = vector() # an empty vector
for (i in 1:100) {
set.seed(i*20)
wcss[i] = sum(kmeans(X, 10)$tot.withinss)
}
plot(20*(1:100), wcss, type = "b", main = paste("Clusters"), xlab = "Initial Seed", ylab = "Within Cluster SS")
which(wcss == min(wcss)) # initial conditions with minimum error## [1] 81insens_init = length(which(wcss == min(wcss)))/100
insens_init## [1] 0.01In the above analysis, we created kmeans models with different k values (from k=2 to k=10) and initialize them from different initialization points by manipulating the random seed. Then, we sum wcss for each time and compare them against to find insensitivity to initialization point.
In our analysis, we have observed that increasing k-value significantly
7. Conclusions
8. Self Reflectance
- Total Hours Spent on this project is about 13 hours.
- We find it very useful to view children with ECC (Erken Cocukluk Curukleri) for motivation.
- We partitioned the project in a cascaded fashion. That means each chapter is assigned to a person. This allowed us to work in a parallel setup, however it makes harder to work remotely.
8. References
- The ECC paper
- stackoverflow.com
- r-bloggers.com
- analyticsvidhya.com